Local heterogeneity of normal lung parenchyma and small airways disease are associated with COPD severity and progression.

BACKGROUND: Small airways disease (SAD) is a major cause of airflow obstruction in COPD patients and has been identified as a precursor to emphysema. Although the amount of SAD in the lungs can be quantified using our Parametric Response Mapping (PRM) approach, the full breadth of this readout as a measure of emphysema and COPD progression has yet to be explored. We evaluated topological features of PRM-derived normal parenchyma and SAD as surrogates of emphysema and predictors of spirometric decline. METHODS: PRM metrics of normal lung (PRMNorm) and functional SAD (PRMfSAD) were generated from CT scans collected as part of the COPDGene study (n = 8956). Volume density (V) and Euler-Poincaré Characteristic (χ) image maps, measures of the extent and coalescence of pocket formations (i.e., topologies), respectively, were determined for both PRMNorm and PRMfSAD. Association with COPD severity, emphysema, and spirometric measures were assessed via multivariable regression models. Readouts were evaluated as inputs for predicting FEV1 decline using a machine learning model. RESULTS: Multivariable cross-sectional analysis of COPD subjects showed that V and χ measures for PRMfSAD and PRMNorm were independently associated with the amount of emphysema. Readouts χfSAD (ß of 0.106, p < 0.001) and VfSAD (ß of 0.065, p = 0.004) were also independently associated with FEV1% predicted. The machine learning model using PRM topologies as inputs predicted FEV1 decline over five years with an AUC of 0.69. CONCLUSIONS: We demonstrated that V and χ of fSAD and Norm have independent value when associated with lung function and emphysema. In addition, we demonstrated that these readouts are predictive of spirometric decline when used as inputs in a ML model. Our topological PRM approach using PRMfSAD and PRMNorm may show promise as an early indicator of emphysema onset and COPD progression.

Computed tomography-based machine learning for donor lung screening before transplantation.

BACKGROUND: Assessment and selection of donor lungs remain largely subjective and experience based. Criteria to accept or decline lungs are poorly standardized and are not compliant with the current donor pool. Using ex vivo computed tomography (CT) images, we investigated the use of a CT-based machine learning algorithm for screening donor lungs before transplantation. METHODS: Clinical measures and ex situ CT scans were collected from 100 cases as part of a prospective clinical trial. Following procurement, donor lungs were inflated, placed on ice according to routine clinical practice, and imaged using a clinical CT scanner before transplantation while stored in the icebox. We trained and tested a supervised machine learning method called dictionary learning, which uses CT scans and learns specific image patterns and features pertaining to each class for a classification task. The results were evaluated with donor and recipient clinical measures. RESULTS: Of the 100 lung pairs donated, 70 were considered acceptable for transplantation (based on standard clinical assessment) before CT screening and were consequently implanted. The remaining 30 pairs were screened but not transplanted. Our machine learning algorithm was able to detect pulmonary abnormalities on the CT scans. Among the patients who received donor lungs, our algorithm identified recipients who had extended stays in the intensive care unit and were at 19 times higher risk of developing chronic lung allograft dysfunction within 2 years posttransplant. CONCLUSIONS: We have created a strategy to ex vivo screen donor lungs using a CT-based machine learning algorithm. As the use of suboptimal donor lungs rises, it is important to have in place objective techniques that will assist physicians in accurately screening donor lungs to identify recipients most at risk of posttransplant complications.

Effects of fetal growth restriction on the perinatal neurovascular unit and possible treatment targets.

The neurovascular unit (NVU) within the brain is a multicellular unit that synergistically acts to maintain blood-brain barrier function and meet cerebral metabolic demand. Recent studies have indicated disruption to the NVU is associated with neuropathology in the perinatal brain. Infants with fetal growth restriction (FGR) are known to be at increased risk of neurodevelopmental conditions including motor, learning, and behavioural deficits. There are currently no neuroprotective treatments for these conditions. In this review, we analyse large animal studies examining the effects of FGR on the perinatal NVU. These studies show altered vascularity in the FGR brain as well as blood-brain barrier dysfunction due to underlying cellular changes, mediated by neuroinflammation. Neuroinflammation is a key mechanism associated with pathological effects in the FGR brain. Hence, targeting inflammation may be key to preserving the multicellular NVU and providing neuroprotection in FGR. A number of maternal and postnatal therapies with anti-inflammatory components have been investigated in FGR animal models examining targets for amelioration of NVU disruption. Each therapy showed promise by uniquely ameliorating the adverse effects of FGR on multiple aspects of the NVU. The successful implementation of a clinically viable neuroprotective treatment has the potential to improve outcomes for neonates affected by FGR. IMPACT: Disruption to the neurovascular unit is associated with neuropathology in fetal growth restriction. Inflammation is a key mechanism associated with neurovascular unit disruption in the growth-restricted brain. Anti-inflammatory treatments ameliorate adverse effects on the neurovascular unit and may provide neuroprotection.

Topologic Parametric Response Mapping Identifies Tissue Subtypes Associated with Emphysema Progression.

RATIONALE AND OBJECTIVES: Small airways disease (SAD) and emphysema are significant components of chronic obstructive pulmonary disease (COPD), a heterogenous disease where predicting progression is difficult. SAD, a principal cause of airflow obstruction in mild COPD, has been identified as a precursor to emphysema. Parametric Response Mapping (PRM) of chest computed tomography (CT) can help distinguish SAD from emphysema. Specifically, topologic PRM can define local patterns of both diseases to characterize how and in whom COPD progresses. We aimed to determine if distribution of CT-based PRM of functional SAD (fSAD) is associated with emphysema progression. MATERIALS AND METHODS: We analyzed paired inspiratory-expiratory chest CT scans at baseline and 5-year follow up in 1495 COPDGene subjects using topological analyses of PRM classifications. By spatially aligning temporal scans, we mapped local emphysema at year five to baseline lobar PRM-derived topological readouts. K-means clustering was applied to all observations. Subjects were subtyped based on predominant PRM cluster assignments and assessed using non-parametric statistical tests to determine differences in PRM values, pulmonary function metrics, and clinical measures. RESULTS: We identified distinct lobar imaging patterns and classified subjects into three radiologic subtypes: emphysema-dominant (ED), fSAD-dominant (FD), and fSAD-transition (FT: transition from healthy lung to fSAD). Relative to year five emphysema, FT showed rapid local emphysema progression (-57.5% ± 1.1) compared to FD (-49.9% ± 0.5) and ED (-33.1% ± 0.4). FT consisted primarily of at-risk subjects (roughly 60%) with normal spirometry. CONCLUSION: The FT subtype of COPD may allow earlier identification of individuals without spirometrically-defined COPD at-risk for developing emphysema.

Spatial transcriptomics analysis of neoadjuvant cabozantinib and nivolumab in advanced hepatocellular carcinoma identifies independent mechanisms of resistance and recurrence.

BACKGROUND: Novel immunotherapy combination therapies have improved outcomes for patients with hepatocellular carcinoma (HCC), but responses are limited to a subset of patients. Little is known about the inter- and intra-tumor heterogeneity in cellular signaling networks within the HCC tumor microenvironment (TME) that underlie responses to modern systemic therapy. METHODS: We applied spatial transcriptomics (ST) profiling to characterize the tumor microenvironment in HCC resection specimens from a prospective clinical trial of neoadjuvant cabozantinib, a multi-tyrosine kinase inhibitor that primarily blocks VEGF, and nivolumab, a PD-1 inhibitor in which 5 out of 15 patients were found to have a pathologic response at the time of resection. RESULTS: ST profiling demonstrated that the TME of responding tumors was enriched for immune cells and cancer-associated fibroblasts (CAF) with pro-inflammatory signaling relative to the non-responders. The enriched cancer-immune interactions in responding tumors are characterized by activation of the PAX5 module, a known regulator of B cell maturation, which colocalized with spots with increased B cell marker expression suggesting strong activity of these cells. HCC-CAF interactions were also enriched in the responding tumors and were associated with extracellular matrix (ECM) remodeling as there was high activation of FOS and JUN in CAFs adjacent to the tumor. The ECM remodeling is consistent with proliferative fibrosis in association with immune-mediated tumor regression. Among the patients with major pathologic responses, a single patient experienced early HCC recurrence. ST analysis of this clinical outlier demonstrated marked tumor heterogeneity, with a distinctive immune-poor tumor region that resembles the non-responding TME across patients and was characterized by HCC-CAF interactions and expression of cancer stem cell markers, potentially mediating early tumor immune escape and recurrence in this patient. CONCLUSIONS: These data show that responses to modern systemic therapy in HCC are associated with distinctive molecular and cellular landscapes and provide new targets to enhance and prolong responses to systemic therapy in HCC.

Quantitative CT of Normal Lung Parenchyma and Small Airways Disease Topologies are Associated With COPD Severity and Progression.

Objectives: Small airways disease (SAD) is a major cause of airflow obstruction in COPD patients, and has been identified as a precursor to emphysema. Although the amount of SAD in the lungs can be quantified using our Parametric Response Mapping (PRM) approach, the full breadth of this readout as a measure of emphysema and COPD progression has yet to be explored. We evaluated topological features of PRM-derived normal parenchyma and SAD as surrogates of emphysema and predictors of spirometric decline. Materials and Methods: PRM metrics of normal lung (PRMNorm) and functional SAD (PRMfSAD) were generated from CT scans collected as part of the COPDGene study (n=8956). Volume density (V) and Euler-Poincaré Characteristic (χ) image maps, measures of the extent and coalescence of pocket formations (i.e., topologies), respectively, were determined for both PRMNorm and PRMfSAD. Association with COPD severity, emphysema, and spirometric measures were assessed via multivariable regression models. Readouts were evaluated as inputs for predicting FEV1 decline using a machine learning model. Results: Multivariable cross-sectional analysis of COPD subjects showed that V and χ measures for PRMfSAD and PRMNorm were independently associated with the amount of emphysema. Readouts χfSAD (ß of 0.106, p<0.001) and VfSAD (ß of 0.065, p=0.004) were also independently associated with FEV1% predicted. The machine learning model using PRM topologies as inputs predicted FEV1 decline over five years with an AUC of 0.69. Conclusions: We demonstrated that V and χ of fSAD and Norm have independent value when associated with lung function and emphysema. In addition, we demonstrated that these readouts are predictive of spirometric decline when used as inputs in a ML model. Our topological PRM approach using PRMfSAD and PRMNorm may show promise as an early indicator of emphysema onset and COPD progression.

Lung cancer lesion detection in histopathology images using graph-based sparse PCA network.

Early detection of lung cancer is critical for improvement of patient survival. To address the clinical need for efficacious treatments, genetically engineered mouse models (GEMM) have become integral in identifying and evaluating the molecular underpinnings of this complex disease that may be exploited as therapeutic targets. Assessment of GEMM tumor burden on histopathological sections performed by manual inspection is both time consuming and prone to subjective bias. Therefore, an interplay of needs and challenges exists for computer-aided diagnostic tools, for accurate and efficient analysis of these histopathology images. In this paper, we propose a simple machine learning approach called the graph-based sparse principal component analysis (GS-PCA) network, for automated detection of cancerous lesions on histological lung slides stained by hematoxylin and eosin (H&E). Our method comprises four steps: 1) cascaded graph-based sparse PCA, 2) PCA binary hashing, 3) block-wise histograms, and 4) support vector machine (SVM) classification. In our proposed architecture, graph-based sparse PCA is employed to learn the filter banks of the multiple stages of a convolutional network. This is followed by PCA hashing and block histograms for indexing and pooling. The meaningful features extracted from this GS-PCA are then fed to an SVM classifier. We evaluate the performance of the proposed algorithm on H&E slides obtained from an inducible K-rasG12D lung cancer mouse model using precision/recall rates, Fß-score, Tanimoto coefficient, and area under the curve (AUC) of the receiver operator characteristic (ROC) and show that our algorithm is efficient and provides improved detection accuracy compared to existing algorithms.

Machine learning for screening of at-risk, mild and moderate COPD patients at risk of FEV1 decline: results from COPDGene and SPIROMICS.

Purpose: The purpose of this study was to train and validate machine learning models for predicting rapid decline of forced expiratory volume in 1 s (FEV1) in individuals with a smoking history at-risk-for chronic obstructive pulmonary disease (COPD), Global Initiative for Chronic Obstructive Lung Disease (GOLD 0), or with mild-to-moderate (GOLD 1-2) COPD. We trained multiple models to predict rapid FEV1 decline using demographic, clinical and radiologic biomarker data. Training and internal validation data were obtained from the COPDGene study and prediction models were validated against the SPIROMICS cohort. Methods: We used GOLD 0-2 participants (n = 3,821) from COPDGene (60.0 ± 8.8 years, 49.9% male) for variable selection and model training. Accelerated lung function decline was defined as a mean drop in FEV1% predicted of > 1.5%/year at 5-year follow-up. We built logistic regression models predicting accelerated decline based on 22 chest CT imaging biomarker, pulmonary function, symptom, and demographic features. Models were validated using n = 885 SPIROMICS subjects (63.6 ± 8.6 years, 47.8% male). Results: The most important variables for predicting FEV1 decline in GOLD 0 participants were bronchodilator responsiveness (BDR), post bronchodilator FEV1% predicted (FEV1.pp.post), and CT-derived expiratory lung volume; among GOLD 1 and 2 subjects, they were BDR, age, and PRMlower lobes fSAD. In the validation cohort, GOLD 0 and GOLD 1-2 full variable models had significant predictive performance with AUCs of 0.620 ± 0.081 (p = 0.041) and 0.640 ± 0.059 (p < 0.001). Subjects with higher model-derived risk scores had significantly greater odds of FEV1 decline than those with lower scores. Conclusion: Predicting FEV1 decline in at-risk patients remains challenging but a combination of clinical, physiologic and imaging variables provided the best performance across two COPD cohorts.

CT-based Machine Learning for Donor Lung Screening Prior to Transplantation.

Background: Assessment and selection of donor lungs remains largely subjective and experience based. Criteria to accept or decline lungs are poorly standardized and are not compliant with the current donor pool. Using ex vivo CT images, we investigated the use of a CT-based machine learning algorithm for screening donor lungs prior to transplantation. Methods: Clinical measures and ex-situ CT scans were collected from 100 cases as part of a prospective clinical trial. Following procurement, donor lungs were inflated, placed on ice according to routine clinical practice, and imaged using a clinical CT scanner prior to transplantation while stored in the icebox. We trained and tested a supervised machine learning method called dictionary learning , which uses CT scans and learns specific image patterns and features pertaining to each class for a classification task. The results were evaluated with donor and recipient clinical measures. Results: Of the 100 lung pairs donated, 70 were considered acceptable for transplantation (based on standard clinical assessment) prior to CT screening and were consequently implanted. The remaining 30 pairs were screened but not transplanted. Our machine learning algorithm was able to detect pulmonary abnormalities on the CT scans. Among the patients who received donor lungs, our algorithm identified recipients who had extended stays in the ICU and were at 19 times higher risk of developing CLAD within 2 years post-transplant. Conclusions: We have created a strategy to ex vivo screen donor lungs using a CT-based machine learning algorithm. As the use of suboptimal donor lungs rises, it is important to have in place objective techniques that will assist physicians in accurately screening donor lungs to identify recipients most at risk of post-transplant complications.

Uncovering the spatial landscape of molecular interactions within the tumor microenvironment through latent spaces.

Recent advances in spatial transcriptomics (STs) enable gene expression measurements from a tissue sample while retaining its spatial context. This technology enables unprecedented in situ resolution of the regulatory pathways that underlie the heterogeneity in the tumor as well as the tumor microenvironment (TME). The direct characterization of cellular co-localization with spatial technologies facilities quantification of the molecular changes resulting from direct cell-cell interaction, as it occurs in tumor-immune interactions. We present SpaceMarkers, a bioinformatics algorithm to infer molecular changes from cell-cell interactions from latent space analysis of ST data. We apply this approach to infer the molecular changes from tumor-immune interactions in Visium spatial transcriptomics data of metastasis, invasive and precursor lesions, and immunotherapy treatment. Further transfer learning in matched scRNA-seq data enabled further quantification of the specific cell types in which SpaceMarkers are enriched. Altogether, SpaceMarkers can identify the location and context-specific molecular interactions within the TME from ST data.

PSA density does not improve predictive accuracy of the UCSF-CAPRA score.

INTRODUCTION: The University of California, San Francisco Cancer of the Prostate Risk Assessment (CAPRA) score is a validated tool using factors at diagnosis to predict prostate cancer outcomes after radical prostatectomy (RP). This study evaluates whether substitution of prostate-specific antigen (PSA) density for serum PSA improves predictive performance of the clinical CAPRA model. METHODS: Participants were diagnosed in 2000-2019 with stage T1/T2 cancer, underwent RP, with at least a 6-month follow-up. We computed standard CAPRA score using diagnostic age, Gleason grade, percent positive cores, clinical T stage, and serum PSA, and an alternate score using similar variables but substituting PSA density for PSA. We reported CAPRA categories as low (0-2), intermediate (3-5), and high (6-10) risk. Recurrence was defined as two consecutive PSA ≥ 0.2 ng/mL or receipt of salvage treatment. Life table and Kaplan-Meier analysis evaluated recurrence-free survival after prostatectomy. Cox proportional hazards regression models tested associations of standard or alternate CAPRA variables with recurrence risk. Additional models tested associations between standard or alternate CAPRA score with recurrence risk. Cox log-likelihood ratio test (-2 LOG L) assessed model accuracy. RESULTS: A total of 2880 patients had median age 62 years, GG1 30% and GG2 31%, median PSA 6.5, and median PSA density 0.19. Median postoperative follow-up was 45 months. Alternate CAPRA model was associated with shifts in risk scores, with 16% of patients increasing and 7% decreasing (p < 0.01). Recurrence-free survival after RP was 75% at 5 years and 62% at 10 years. Both CAPRA component models were associated with recurrence risk after RP on Cox regression. Covariate fit statistics showed better fit for standard CAPRA model versus alternate (p < 0.01). Standard (hazard ratio [HR]: 1.55; 95% confidence interval [CI]: 1.50-1.61) and alternate (HR: 1.50; 95% CI: 1.44-1.55) CAPRA scores were associated with recurrence risk, with better fit for standard model (p < 0.01). CONCLUSIONS: In a 2880 patient cohort followed for median 45 months after RP, alternate CAPRA model using PSA density was associated with higher biochemical recurrence (BCR) risk, but performed inferior to standard CAPRA at predicting BCR. While PSA density is an established prognostic variable in prediagnostic settings and sub-stratifying low-risk disease, it does not improve BCR model predictive accuracy when applied across a range of cancer risk.

Radiation-induced Fistulas in Patients With Prior Pelvic Radiotherapy for Prostate Cancer: A Systematic Review and Meta-analysis.

OBJECTIVE: To systematically aggregate and summarize existing data on fistula prevalence among patients with a history of pelvic radiotherapy for prostate cancer. MATERIALS AND METHODS: We queried PubMed, Embase, and Web of Science on October 7, 2020 for peer-reviewed publications pertaining to radiation-induced fistulas in the pelvis. For meta-analysis, we used the random-effects model. We used the I2 statistic to quantify heterogeneity and the Newcastle-Ottawa Scale to assess risk of bias. RESULTS: Our final meta-analysis included 6 cohort studies with a total of 7665 patients exposed to pelvic radiotherapy between 1967 and 2013. Median follow-up time was 35.5 months (IQR 33.5-57.5). Pooled prevalence of radiation-induced fistula across all 6 cohort studies was 0.2% (95% CI: 0.1-0.4, I2 = 0.000%, P < .608). In subgroup analysis, we did not detect significant heterogeneity in fistula prevalence in patients who were re-irradiated (0.3%, 95% CI: 0.1-0.4; P = .762) or patients on concurrent chemotherapy (0.4%, 95% CI: -0.3 -1.2; P = .664) compared to those receiving their first course of radiotherapy alone. No randomized controlled trials met inclusion criteria due to ambiguous and inconsistent reporting language for fistula occurrence. CONCLUSION: There is limited published literature reporting fistula as an adverse event of prostate cancer radiotherapy, especially in the medium and long-term period. Patients undergoing pelvic radiotherapy for prostate cancer appear at low short-term risk for developing fistulas. Adverse event reporting in randomized controlled trials merits greater granularity where fistulas should be reported with specificity rather than aggregating into broad categories of genitourinary or gastrointestinal adverse events.

Analysis of Female Participant Representation in Registered Oncology Clinical Trials in the United States from 2008 to 2020.

BACKGROUND: Female underrepresentation in oncology clinical trials can result in outcome disparities. We evaluated female participant representation in US oncology trials by intervention type, cancer site, and funding. MATERIALS AND METHODS: Data were extracted from the publicly available Aggregate Analysis of ClinicalTrials.gov database. Initially, 270,172 studies were identified. Following the exclusion of trials using Medical Subject Heading terms, manual review, those with incomplete status, non-US location, sex-specific organ cancers, or lacking participant sex data, 1650 trials consisting of 240,776 participants remained. The primary outcome was participation to prevalence ratio (PPR): percent females among trial participants divided by percent females in the disease population per US Surveillance, Epidemiology, and End Results Program data. PPRs of 0.8-1.2 reflect proportional female representation. RESULTS: Females represented 46.9% of participants (95% CI, 45.4-48.4); mean PPR for all trials was 0.912. Females were underrepresented in surgical (PPR 0.74) and other invasive (PPR 0.69) oncology trials. Among cancer sites, females were underrepresented in bladder (odds ratio [OR] 0.48, 95% CI 0.26-0.91, P = .02), head/neck (OR 0.44, 95% CI 0.29-0.68, P < .01), stomach (OR 0.40, 95% CI 0.23-0.70, P < .01), and esophageal (OR 0.40 95% CI 0.22-0.74, P < .01) trials. Hematologic (OR 1.78, 95% CI 1.09-1.82, P < .01) and pancreatic (OR 2.18, 95% CI 1.46-3.26, P < .01) trials had higher odds of proportional female representation. Industry-funded trials had greater odds of proportional female representation (OR 1.41, 95% CI 1.09-1.82, P = .01) than US government and academic-funded trials. CONCLUSIONS: Stakeholders should look to hematologic, pancreatic, and industry-funded cancer trials as exemplars of female participant representation and consider female representation when interpreting trial results.

Endothelial colony forming cell administration promotes neurovascular unit development in growth restricted and appropriately grown fetal lambs.

BACKGROUND: Fetal growth restriction (FGR) is associated with deficits in the developing brain, including neurovascular unit (NVU) dysfunction. Endothelial colony forming cells (ECFC) can mediate improved vascular stability, and have demonstrated potential to enhance vascular development and protection. This investigation examined whether ECFCs from human umbilical cord blood (UCB) enhanced NVU development in FGR and appropriate for gestational age (AGA) fetal sheep. METHODS: Twin-bearing ewes had surgery performed at 88-90 days' gestation, inducing FGR in one fetus. At 113 days, ECFCs (1 × 107 cells) cultured from human UCB were administered intravenously to fetal sheep in utero. At 127 days, ewes and their fetuses were euthanised, fetal brains collected, and NVU components analysed by immunohistochemistry. RESULTS: Twenty-four fetal lambs, arranged in four groups: AGA (n = 7), FGR (n = 5), AGA + ECFC (n = 6), and FGR + ECFC (n = 6), were included in analyses. FGR resulted in lower body weight than AGA (P = 0.002) with higher brain/body weight ratio (P = 0.003). ECFC treatment was associated with increased vascular density throughout the brain in both AGA + ECFC and FGR + ECFC groups, as well as increased vascular-astrocyte coverage and VEGF expression in the cortex (P = 0.003, P = 0.0006, respectively) and in the subcortical white matter (P = 0.01, P = 0.0002, respectively) when compared with the untreated groups. CONCLUSIONS: ECFC administration enhanced development of NVU components in both the AGA and FGR fetal brain. Further investigation is required to assess how to optimise the enhanced angiogenic capabilities of ECFCs to provide a therapeutic strategy to protect the developing NVU against vulnerabilities associated with FGR.

Spatial transcriptomics analysis of neoadjuvant cabozantinib and nivolumab in advanced hepatocellular carcinoma identifies independent mechanisms of resistance and recurrence.

Novel immunotherapy combination therapies have improved outcomes for patients with hepatocellular carcinoma (HCC), but responses are limited to a subset of patients and recurrence can also occur. Little is known about the inter- and intra-tumor heterogeneity in cellular signaling networks within the HCC tumor microenvironment (TME) that underlie responses to modern systemic therapy. We applied spatial transcriptomics (ST) profiling to characterize the tumor microenvironment in HCC resection specimens from a clinical trial of neoadjuvant cabozantinib, a multi-tyrosine kinase inhibitor that primarily blocks VEGF, and nivolumab, a PD-1 inhibitor in which 5 out of 15 patients were found to have a pathologic response. ST profiling demonstrated that the TME of responding tumors was enriched for immune cells and cancer associated fibroblasts (CAF) with pro-inflammatory signaling relative to the non-responders. The enriched cancer-immune interactions in responding tumors are characterized by activation of the PAX5 module, a known regulator of B cell maturation, which colocalized with spots with increased B cell markers expression suggesting strong activity of these cells. Cancer-CAF interactions were also enriched in the responding tumors and were associated with extracellular matrix (ECM) remodeling as there was high activation of FOS and JUN in CAFs adjacent to tumor. The ECM remodeling is consistent with proliferative fibrosis in association with immune-mediated tumor regression. Among the patients with major pathologic response, a single patient experienced early HCC recurrence. ST analysis of this clinical outlier demonstrated marked tumor heterogeneity, with a distinctive immune-poor tumor region that resembles the non-responding TME across patients and was characterized by cancer-CAF interactions and expression of cancer stem cell markers, potentially mediating early tumor immune escape and recurrence in this patient. These data show that responses to modern systemic therapy in HCC are associated with distinctive molecular and cellular landscapes and provide new targets to enhance and prolong responses to systemic therapy in HCC.

Harnessing anti-cytomegalovirus immunity for local immunotherapy against solid tumors.

Tumor infiltration by T cells profoundly affects cancer progression and responses to immunotherapy. However, the tumor immunosuppressive microenvironment can impair the induction, trafficking, and local activity of antitumor T cells. Here, we investigated whether intratumoral injection of virus-derived peptide epitopes could activate preexisting antiviral T cell responses locally and promote antitumor responses or antigen spreading. We focused on a mouse model of cytomegalovirus (CMV), a highly prevalent human infection that induces vigorous and durable T cell responses. Mice persistently infected with murine CMV (MCMV) were challenged with lung (TC-1), colon (MC-38), or melanoma (B16-F10) tumor cells. Intratumoral injection of MCMV-derived T cell epitopes triggered in situ and systemic expansion of their cognate, MCMV-specific CD4+ or CD8+ T cells. The MCMV CD8+ T cell epitopes injected alone provoked arrest of tumor growth and some durable remissions. Intratumoral injection of MCMV CD4+ T cell epitopes with polyinosinic acid:polycytidylic acid (pI:C) preferentially elicited tumor antigen-specific CD8+ T cells, promoted tumor clearance, and conferred long-term protection against tumor rechallenge. Notably, secondary proliferation of MCMV-specific CD8+ T cells correlated with better tumor control. Importantly, intratumoral injection of MCMV-derived CD8+ T cell-peptide epitopes alone or CD4+ T cell-peptide epitopes with pI:C induced potent adaptive and innate immune activation of the tumor microenvironment. Thus, CMV-derived peptide epitopes, delivered intratumorally, act as cytotoxic and immunotherapeutic agents to promote immediate tumor control and long-term antitumor immunity that could be used as a stand-alone therapy. The tumor antigen-agnostic nature of this approach makes it applicable across a broad range of solid tumors regardless of their origin.

Mobile Clinical Trial Matching Technology in Medical Oncology Clinic: A Pilot Feasibility Study.

PURPOSE: The internet is a common source of health information for patients and can be leveraged to provide patient-facing clinical trial information. This pilot study integrated an online prostate cancer clinical trial matching technology, called Trial Library (TL), in an academic medical oncology clinic from February 2019 to April 2021. PATIENTS AND METHODS: This is a single-arm interventional pilot study among patients with a known prostate cancer diagnosis. Participants were given access to TL before seeing a provider. The primary and secondary study end points were the overall satisfaction with TL and the proportion of participant-initiated clinical trial discussion with providers after exposure to TL, respectively. The null hypothesis or true satisfaction rate (acceptability) was tested against a one-sided alternative and was rejected if 29 or more satisfactions were observed. RESULTS: Among 272 patients approached, 66 provided informed consent to participate in the study. The mean age was 70.8 years (standard deviation = 7.9). The majority of participants were White (82%) and had metastases present at the time of enrollment (65%). The baseline clinical trial discussion rate ascertained via electronic medical record review was 28%. After accessing TL, a significantly larger proportion of participants (48.5%) discussed clinical trials during the clinic visit (P = .007), half of which were patient-initiated. The majority of participants indicated that TL increased their interest in clinical trials (68.2%); however, satisfaction/extreme satisfaction with the technology was 38%. CONCLUSION: Access to TL resulted in a significant increase in patient-initiated discussions regarding clinical trials and an increase in interest in clinical trial participation although these data do not address if this resulted in increased accrual to clinical trials. The satisfaction rate did not meet the target to reject the null hypothesis, suggesting the need for iterative design of patient-facing health information.

Quantitative CT Correlates with Local Inflammation in Lung of Patients with Subtypes of Chronic Lung Allograft Dysfunction.

Chronic rejection of lung allografts has two major subtypes, bronchiolitis obliterans syndrome (BOS) and restrictive allograft syndrome (RAS), which present radiologically either as air trapping with small airways disease or with persistent pleuroparenchymal opacities. Parametric response mapping (PRM), a computed tomography (CT) methodology, has been demonstrated as an objective readout of BOS and RAS and bears prognostic importance, but has yet to be correlated to biological measures. Using a topological technique, we evaluate the distribution and arrangement of PRM-derived classifications of pulmonary abnormalities from lung transplant recipients undergoing redo-transplantation for end-stage BOS (N = 6) or RAS (N = 6). Topological metrics were determined from each PRM classification and compared to structural and biological markers determined from microCT and histopathology of lung core samples. Whole-lung measurements of PRM-defined functional small airways disease (fSAD), which serves as a readout of BOS, were significantly elevated in BOS versus RAS patients (p = 0.01). At the core-level, PRM-defined parenchymal disease, a potential readout of RAS, was found to correlate to neutrophil and collagen I levels (p < 0.05). We demonstrate the relationship of structural and biological markers to the CT-based distribution and arrangement of PRM-derived readouts of BOS and RAS.